In silico based re-engineering of a computationally designed biosensor with altered signalling mode and improved dynamic range.

A current challenge in the rational design of biomolecular sensors is the ability to custom design binding affinities and detection mode in silico. To this end, we re-engineered a previously reported computationally-designed fluorescent maltooligosaccharide (MOS)-detecting biosensor to both alter its ligand-binding affinity and to analyse the underlying sensing mechanism. The dynamic range of the biosensor was expanded through the computer aided introduction of a series of amino acid substitutions in the starting protein scaffold (MalX from Streptococcus pneumoniae), which generated a biosensor set with binding affinities spanning over five orders of magnitude. The impact of the introduced substitutions on the underlying mode of signal generation was assessed in silico using our previously reported Computational Identification of Non-disruptive Conjugation sites (CINC) pipeline. CINC utilizes molecular dynamics simulations and an in-house developed algorithm to examine and exploit the structural dynamics of a protein at amino acid-level resolution. Using CINC, we demonstrate that re-engineering of the MOS-detecting biosensor set resulted in sensors with two distinct output modes which differed based on local conformational changes at the fluorescently modified reporter position. These output modes were classified as "ligand-sensing"-type biosensors (readout based on the tool sensing a unique conformation in the ligand-bound state), and "apo-sensing"-type biosensors (readout based on the tool sensing a unique conformation in the apo state). Together, these results demonstrate that structural dynamics at the individual amino acid residue level can be used as an engineer-able feature to rationally alter the fluorescence reporting properties of a biosensing device. Moving forward, the CINC workflow can also be adapted for the rational design of protein dynamic properties maximizing its utility as an in silico design platform for custom biomolecular tools.